Method and apparatus for monitoring operational performance of fluid storage systems

ABSTRACT

A method of monitoring a fluid storage and dispensing system having a measurement apparatus for measuring a volume of fluid associated with the system and a plurality of temperature sensing devices disposed at a plurality of locations within the system. The method includes collecting a plurality of measurement data from the measurement apparatus and the plurality of temperature sensing devices in a form readable by a computer; storing the plurality of measurement data in a compressed matrix format in a computer memory; and statistically analyzing the compressed matrix format to determine operational monitoring information and to calculate the volume of fluid based on the measurement data collected from the measurement apparatus and the plurality of temperature sensing devices.

BACKGROUND

[0001] 1. Description of the Background

[0002] The invention relates to monitoring the operational performanceof fluid storage systems.

[0003] Large quantities of liquids and similar materials are oftenstored in bulk storage containers or tanks, which may be locatedabove-ground, partially above-ground, or completely below ground. Suchcontainers or tanks are generally connected by piping to flow-meters ordispensers.

[0004] For example, underground storage tanks (UST's) and, occasionally,above-ground storage tanks (AST's) are used to store petroleum productsand fuel to be dispensed at automobile service stations, truckingterminals, automobile rental outlets, and similar operations throughgasoline, diesel, or kerosene dispensing pumps. Fuel product isgenerally delivered to such facilities by a gravity drop from acompartment in a wheeled transport means such as a fuel delivery truckor an introduction of product through an underground piping system.AST's or UST's are often located at central distribution locations sothat product can be subsequently withdrawn from the tank system to betransported for delivery to a variety of such facilities. A distributionlocation with UST's and AST's may receive deliveries of product from,e.g., a pipeline spur, wheeled transport, a barge, or a rail car.

[0005] Direct observation of the operating condition of such tanks andstorage containers is difficult or impossible. The various methods foridentifying the amount of product in tank systems have varying levels ofaccuracy, repeatability, and performance. Moreover, the accuracy ofdevices which measure the amount of product dispensed from the storagecontainers and tanks differs greatly, and may or may not be temperaturecompensated. The amount of product actually delivered to the tank systemis often measured inaccurately and, frequently, not at all. Rather, theowner or operator of the tank or vessel usually records the invoicedamount of product delivered as the actual amount introduced to the tanksystem, without having any means of confirming whether the invoicedamount of product delivered is correct.

[0006] Consequently, effective management of such facilities iscomplicated by the numerous errors in the various measuring devices andprocedures used to establish a baseline for management, planning anddecision making. Effective management requires the following:

[0007] Accurate measurement of the volume stored in the system.

[0008] Accurate determination of the volume dispensed from the system.

[0009] Accurate determination of the amount of product introduced intothe system.

[0010] Identification of volumes added to or removed from the tanksystem which are not otherwise recorded.

[0011] Rapid identification of leakage from the tank system.

[0012] Continuous monitoring and diagnosis of the operating performanceof all of the component measuring devices of the system.

[0013] Continuous analysis of sales data to predict demands of productfrom the system.

[0014] Determination of optimal reorder times and quantities as afunction to ordering, transportation, holding, and penalty costs inorder to minimize total costs of operation and/or to maximize profits.

[0015] Traditionally, these functions were performed crudely, or, inmany cases, not at all. Volume measurements were, and in many instancesstill are, based on imperfect knowledge of the geometry, dimensions, andconfiguration of the storage vessel. Also, dispensing meters arefrequently miscalibrated. This is true even when tank systems areregulated, due to the breadth of tolerance permitted for individualsales as related to total tank volume. For example, deliveries from thedelivery vehicle are almost always unmetered, additions of product fromdefueling vehicles are typically undocumented, and theft of the productis not uncommon.

[0016] Leakage of product, has in recent years, assumed a dimension farin excess of the mere loss of the product. Environmental damage can, andfrequently does, expose the operator to very large liabilities fromthird party litigation in addition to U.S. Environmental ProtectionAgency (EPA)-mandated remediation which can cost in the range ofhundreds of thousands of dollars. The EPA's requirements for leakdetection are set forth in EPA Pub. No. 510-K-95-003, Straight Talk onTanks: Leak Detection Methods For Petroleum Underground Storage Tanksand Piping (July 1991), which is incorporated herein by reference.

[0017] To address these concerns, Statistical Inventory Reconciliation(SIR) was developed. The SIR method consists of a computer-basedprocedure which identifies all of the sources of error noted above bystatistical analysis of the various and unique patterns that areintroduced into the inventory data and, in particular, into thecumulative variances in the data when viewed as functions of productheight, sales volume, and time.

SUMMARY OF THE INVENTION

[0018] An embodiment of the present invention relates to a method ofmonitoring a fluid storage and dispensing system having a measurementapparatus for measuring a volume of fluid associated with the system anda plurality of temperature sensing devices disposed at a plurality oflocations within the system. The method includes collecting a pluralityof measurement data from the measurement apparatus and the plurality oftemperature sensing devices in a form readable by a computer; storingthe plurality of measurement data in a compressed matrix format in acomputer memory; and statistically analyzing the compressed matrixformat to determine operational monitoring information and to calculatethe volume of fluid based on the measurement data collected from themeasurement apparatus and the plurality of temperature sensing devices.

DESCRIPTION OF THE DRAWINGS

[0019]FIG. 1 is a schematic diagram of a facility including anunderground tank storage system.

[0020]FIGS. 2, 3 and 4 are a portion of the Mathcad computer code usedto perform the data compression algorithm.

[0021]FIGS. 5, 6 and 7 are a block diagram of the steps performed duringroutine operation of the algorithm of the present invention.

[0022]FIG. 8 is a block diagram of the steps performed during the datadeletion operation of the algorithm of the present invention.

[0023]FIG. 9 is a block diagram of the steps performed during thedelivery calculation operation of the algorithm of the presentinvention.

[0024]FIG. 10 is a schematic diagram of a data acquisition andtransmission network that may be used in conjunction with the presentinvention.

[0025]FIG. 11 is a schematic diagram of a facility including anabove-ground tank storage system.

[0026]FIG. 12 is a schematic diagram of a facility including a partiallyabove-ground tank storage system.

[0027]FIG. 13 is a schematic diagram of an underground storage tankfacility including a fuel access control unit.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] The method and apparatus described herein applies to UST's, AST'sor any type of storage tank. The product stored in the tank may be anyfluid, including dry particles that flow in the manner of a fluid.

[0029]FIG. 1 shows a UST facility 10, illustrated as an automobileservice station. Facility 10 includes a series of UST's 12, 14, 16 whichmay store the same or different types of liquid fuel product 18.Volumetric tank gauges 20, 22, 24 in each tank measure the height ofproduct 18 in the tank. Submersible pumps 26, 28, 30 in each tank pumpproduct 18 to one of dispensing pumps 32, 34 through piping lines 36,38, 40. Alternately, facility 10 may be an AST facility withabove-ground tank 1000, as shown in FIG. 11, or a facility with apartially above-ground tank 1010, as shown in FIG. 12.

[0030] Tank gauges 20, 22, 24 are mounted in tanks 12, 14, 16. The tankgauges may consist of or be based on magnetostrictive tank probes orother sensing technologies. In the case of magnetostrictive technology,two floats 42, 44 surround each probe, e.g., gauge 20 in tank 12. Onefloat 42 floats on the upper surface of product 18 in tank 12, and theother float 44 floats on the interface of product 18 with any water orother foreign material collected at the bottom of tank 12. Tank gauge 20determines the distance between floats 42, 44 to obtain the height ofproduct 18 in tank 12. Tank gauge 20 also contains temperature sensors46, 48, 50 spaced along its length to monitor the temperature of product18 at various depth levels.

[0031] Each of the dispensing pumps 32, 34 consists of a totalizer orflow meter 52, 54 disposed in a housing 56, 58 to measure the volume ofproduct 18 dispensed through hoses 60, 62 and nozzles 64, 66. To operatedispensing pump 32, nozzle 64 is removed from housing 56, which actuatesdispensing pump 32 and causes product 18 to flow through hose 60 due tothe pumping action of submersible pumps, 26, 28, 30. A value stored intotalizer 52 is incremented as fuel is dispensed through hose 60. Uponcompletion of the transaction, nozzle 64 is replaced in housing 56,thereby turning off dispensing pump 32 and discontinuing the action ofsubmersible pumps 26, 28, 30 and totalizer 52.

[0032] Transactions are recorded electronically by software in a salesrecording device 71 connected to totalizers 52, 54 of dispensing pumps32, 34. Totalizers 52, 54 in dispensing pumps 32, 34 are connected tosales recording device 71 by means of communications and power supplywires 78, 80.

[0033] Sales recording device 71 contains software capable of emulatingthe functions of a point of sale (POS) terminal associated with fuelsales made at facility 10. POS emulation software in sales recordingdevice 71 functions on the basis of read only commands to eliminate thepossibility of conflict with control commands from a POS terminalemployed by facility 10. Alternative data acquisition systems can resultin destruction of credit card sales records, inadvertently shutting downthe entire system, and/or causing electrical interference in the pumplinks.

[0034] Tank gauges 20, 22, 24 are connected to a tank monitor 82 bymeans of communications and power supply wires 84, 86, 88 or communicatedata through radio frequency transmission. Tank monitor 82 converts rawdata obtained from tank gauges 20, 22, 24 into a form usable by acomputer.

[0035] A computer 70 contains a processor 72 capable of running variouscomputer software applications and a memory 74. Tank monitor 82 andsales recording device 71 are electrically connected to computer 70 torelay totalizer values, product height and temperature data to computer70. Software executable by processor 72 of computer 70 is capable ofquerying tank monitor 82 and sales recording device 71 to obtainmeasurement data at selected time intervals. The data is continuouslyevaluated as it is collected and is stored in memory 74 of computer 70for later retrieval and detailed analysis. Alternatively, computer 70may communicate with a host processor 90 at a remote location. Thecontinuous evaluations or detailed analysis may then be performed byhost processor 90, which may be faster or more efficient than computer70.

[0036] As an example, computer 70 may be a personal computer or anyother proprietary microprocessor-based unit. Computer 70 may capturedata automatically through direct-connect serial interfaces or otherdata carrying transmission with tank monitor 82 and sales recordingdevice 71, or by manual operator keypad entry. According to anembodiment, computer 70 communicates with equipment at facility 10through four programmable serial communication ports, such as RS-232communication ports. According to other embodiments, computer 70 maycapture data wirelessly through, for example, PCS, cellular, Bluetooth,802.11, WiFi, Infrared, radio frequency, or other wireless technology.

[0037] Computer 70 may, e.g., store tank dimensions and productcharacteristics, and concurrent time and date data along with themeasurement data. Computer 70 may be used to produce error and analysisreports as calculated by the software. It may also have alarmevent-initiated capabilities, such as when a leak is detected in any ofthe tanks. Such a computer system can accommodate facility and customerspecific requirements while maintaining complete compatibility withother system components.

[0038] The SIR method involves reconciling volume data obtained fromtank monitor 82 and volume data obtained from sales records. Salestransactions may be detected in a number of ways, including anelectronic signal emitted from totalizers 52, 54, by voltage sensing ofcontrol relays on pump dispensers 32, 34, or by observation of productremoval using tank gauges 20, 22, 24.

[0039] It is essential that the measurements used to obtain these twotypes of data are made simultaneously. The SIR method of the presentinvention collects and analyzes observations of sales volumes and tankvolumes which are derived simultaneously. Failure to collect both typesof data simultaneously would bias estimates derived from separate volumemeasurements.

[0040] The SIR method properly accounts for the effects of temperature,pressure and specific gravity. In addition, product from two or moretanks may be blended, such as to achieve varying petroleum octane levelsat pump dispensers 32, 34. When different fluid products are blended,the tanks are treated as one unit, and an additional parameter isintroduced to determine the actual blend percentages.

[0041] Data concerning the physical characteristics of the tankconfigurations and the accuracy of the various gauges and meteringdevices is collected during the installation and a set-up phase ofoperation of facility 10 to create a basis for subsequent statisticalanalysis. Information is then continuously collected so that thestatistical analysis of SIR can be performed by computer 70 or hostprocessor 90.

[0042] Several procedures are used either singly or in combination toobtain the volume observations. First, where the system configurationprovides for determining whether hoses and dispensers associated with agiven tank are active, the system is queried on a minute-by-minutebasis, or on the basis of another predetermined time interval, todetermine the status of the dispensers. When all of the dispensers areidle, the values from totalizers 52, 54, the tank volumes (i.e. productheights in the tanks) and temperatures are recorded.

[0043] Second, submersible pumps 26, 28, 30 are checked to determineon/off status. When is it determined that the pumps are turned off, thevalues from totalizers 52, 54 are read, and tank volumes andtemperatures are recorded.

[0044] Third, software algorithms used by computer 70 detect and measureleads and/or lags between the recording of sales events andcorresponding gauge and meter readings. When leads or lags areencountered and constitute a physical characteristic of the datameasurement and recording system, constrained optimization, rather thanunconstrained optimization, may be used to determine parameterestimates. Lagrange multipliers are one example of such a constrainedoptimization method.

[0045] The method of the present invention is capable of providingdynamic monitoring of system performance. For example, the leakdetection function is carried out continuously while normal operations,e.g., removals and deliveries, are taking place. To detect leaksdynamically, the software is programmed to detect when sales or deliveryevents occur and to calculate the volumes of product removed or added asa result of such activities. Thus, dynamic testing does not require thatthe system be dormant and addresses the entire system from the point offilling to the point of dispensing.

[0046] The SIR method of the present invention also distinguishesbetween one-time removals and continuous losses consistent with leakage.The integrity or leak-free status of the system is not assumed a priori.Instead, the individual and unique characteristic pattern induced byeach form of error when viewed along the separate dimensions of time,product height and sales volume are used to identify and quantify theerrors. The method may also be used to detect and quantify undocumentedremovals, e.g., theft or additions of product.

[0047] Further, the overall system is self diagnosing in that itdetermines from the data the maximum degrees of reliability andprecision of which a particular operating configuration is capable atany given time, as well as the degree of calibration accuracy.

[0048] In particular, product height in the tanks and temperature aremeasured continuously at, e.g., one-minute intervals. Height and grossvolumes are converted to net volumes at, e.g., 60° F. or 15° C., usingthe algorithms described below. Sales recorded by the totalizers 52, 54are extracted and stored in memory 74 at times coincident with readingsfrom tank gauges 20, 22, 24. If the dispensing system is capable oftransmitting a signal indicating whether or not any or all individualhoses are active, that information is also stored in memory 74coincident with taking gauge and meter readings.

[0049] The method of the present invention is designed to achieve themaximum accuracy possible within the limitations imposed by the inherentrandom and irreducible noise in the various measuring devicesincorporated. It utilizes multiple measurements over extended timeperiods to identify and quantify systematic and repeatable effects inthe instrumentation and thereby correct for such effects using the knownphysical characteristics of the devices. The system makes no a prioriassumptions as to the accuracy of the devices used to measure productvolume in the tank, to measure volumes removed, or as to the accuracy ofvolumes reported to have been delivered into the system.

[0050] The resulting volumetric calculations are independent of thephysical characteristics of the tank configuration and the variousmeasuring devices which may be incorporated into the system. The resultsdo not rely on input entered externally by the operator or fromdiagnostics internal to the measuring devices used. Instead, the outputproduced by the software which analyzes the measured data depends onlyon the patterns induced in inventory data produced by the tank gaugesand measuring devices and, in particular, the cumulative variances thatresult when the various input values are combined.

[0051] Various error patterns which the measuring devices can induce andthe effects of temperature, tank geometry, and orientation on cumulativevariances are derived from empirical analysis of real-world inventorydata. The system's software synthesizes the output measurements of thevarious devices based on known characteristics derived from theempirical data. Thus, the software is capable of identifying measurementerrors caused by the measuring devices and simultaneously compensatingfor the effects of those errors.

[0052] Gauges can be systematically inaccurate in two ways. The heightof the product in the tank can be incorrect, and the height to volumeconversion algorithms may not reflect accurately the true dimensions ofthe tank or its orientation in the ground. The latter may be the resultof incorrect measurements or an inappropriate conversion algorithm.

[0053] The presence of such systematic effects and their nature may beestablished by examining the pattern of inventory variances as afunction of product height. Errors of this kind induce patterns whichrepeat themselves as the tank is filled and emptied. If the tank lengthis incorrect, a linear pattern is induced. If product height is inerror, a curvilinear pattern results reflecting the varying volumes indifferent cross sections of a cylindrical tank. Tilt along the length ofthe tank induces a sinusoidal pattern symmetrical about the mid-heightof the tank. Absent such errors, the pattern will be purely random,reflecting only the inherent noise of the measuring devices. The absenceof randomness and the presence of a systematic pattern serves toidentify the presence of systematic error. The pattern of a departurefrom random and its extent determines the source and extent of thesystematic effects and the means necessary to correct them.

[0054] Dispensing errors, unlike volume measuring errors, areindependent of product height, but are sensitive to the volume ofproduct dispensed. The nature and extent of dispensing errors can beestablished by examining inventory variances as a function of salesvolume. As in the case of volume measurements, in the absence ofsystematic errors, variances as a function of sales volume will berandom. The form and extent of departures from randomness serve todetermine the source and extent of the errors and provide for theirremoval.

[0055] Leakage from the system creates a continuous downward trend inthe cumulative variance when viewed as a function of time. By contrast,one-time additions and removals of product cause significant upward ordownward translations of the cumulative variance which remainpermanently in the record and do not introduce a continuous trend.Leakage is distinguishable from tank gauging errors when viewed as afunction of product height because the pattern does not repeat as thetank is filled and emptied. If product is leaking from the system, aseries of parallel translations in the cumulative variance is generated,each shifted by the volume of product lost between deliveries.

[0056] The accuracy of measurements taken from the various components ofthe system determines the accuracy achievable in any one individualobservation. Since the leak rate is computed from a series of successiveobservations, however, the minimum detectable leak rate can be reducedto any desired magnitude by increasing the number of successiveobservations recorded. Thus, the system can serve as a finalverification for leakage indications obtained by other methods.

[0057] At the conclusion of an initial set up period of data collectionincluding one or more delivery and sales cycles, the collectedmeasurement data is analyzed by regression analysis. The initial set-upregression is used to derive tank dimensions and orientation, individualmeter calibrations and secular trends. A confidence level value p iscomputed at the 0.01 level of significance to determine the minimum leakrate detectable by the system, and the residual variance is computed toprovide the current noise level of the system.

[0058] The regression is performed according to the following equation:${{st}_{i}\left( {R,L,T} \right)} = {a - {\sum\limits_{j = 1}^{i}{\sum\limits_{k = 1}^{n}{\alpha_{k}S\quad a_{k\quad j}}}} + {\sum\limits_{j = 1}^{i}D_{j}} - {E\quad t_{i}L\quad s} + {\sum\limits_{j = 1}^{m}{B_{j}I_{i\quad j}}}}$

[0059] where:

[0060] st_(i)(R,L,T)=Volume in gallons derived from the ith gaugereading in inches in a cylindrical tank with or without hemisphericalend caps with radius R, length L, and tilt over its length of T inches.

[0061] a=Initial inventory in gallons, which is to be estimated.

[0062] Sa_(kj)=Sales volume recorded on the kth totalizer.

[0063] a_(k)=Fraction of sales volume recorded on the kth totalizeractually removed from the tank, which is to be estimated.

[0064] D_(j)=Volume of the jth delivery.

[0065] Et_(i)=Elapsed time since initiation until the ith gauge readingis recorded.

[0066] L_(s)=Constant gain or loss in product per unit of time.

[0067] B_(j)=Volume of product added (e.g. delivery) or removed duringsome discrete time interval prior to or during observation period j.$I_{ij} = \left\{ \begin{matrix}0 & \text{if} & {j < i} \\1 & \text{if} & {j \geq i}\end{matrix} \right.$

[0068] All of the parameters are estimated simultaneously using leastsquare estimation procedures. The R and T parameters are derivednumerically, but the other parameters are derived analytically.

[0069] Further, all of the parameters, including the initial inventory,are estimated simultaneously. The initial volume must be estimated fromall succeeding data, even if the tank is initially empty, otherwise theinitial gauge reading and its conversion to gallons is assigned acredibility not assumed for all succeeding readings. Also, in a greatmajority of applications, the initial inventory in an already existingand operating system is not accurately known.

[0070] Initial inventory estimation is vital in determining the geometryof the tank. When tank geometry, tank orientation, or tank productheight measurement depart from the values obtained from nominal sources,all gauge and meter measurements are affected. It is practicallyimpossible to detect the errors induced in the gauge measurements andcorrect for them unless the estimation of the initial inventory is madecoincident with the estimation of the values of the other parameters.

[0071] The estimate of the parameters are based on the totality of thedata collected. This means, e.g., that the estimate of leak rate Ls isdetermined from a linear trend including all of the data collected, notmerely at one end of the reconciliation period. Likewise, estimates oftank dimensions and orientation are derived from their overallcontribution to reduction in residual variance, as opposed to a sale bysale analysis of tank segments.

[0072] The volume st_(i) (R,L,T) is derived from the product heightmeasurement by multiplying the constant area of tank segments of heighth (in inches) by tank length L. The volume in gallons of product in ahorizontal cylindrical tank of radius R is given by:${Vol} = {\frac{L}{231}\left\lbrack {{R^{2}{\cos^{- 1}\left( \frac{R - h}{R} \right)}} - {\left( {R - h} \right)\left( {{2R\quad h} - h^{2}} \right)^{\frac{1}{2}}}} \right\rbrack}$

[0073] In the case of a tilted tank, the area of the segments varieswith position along the length of the titled tank, and the volume isdetermined by integrating over the length L. Such integration does notresult in a closed form because the cross sections are not circular, anda numerical integration would severely limit the frequency ofobservations. Instead, in this application the tank is treated as lyinghorizontally and the product is considered tilted, to derive anequivalent volume. This integration yields the closed form:${Vol} = {\frac{R^{3}}{231}\left\lbrack {{\left( {z - 1} \right){\sin^{- 1}\left( {{2z} - z^{2}} \right)}^{\frac{1}{2}}} - {\frac{1}{3}\left( {{2z} - z^{2}} \right)^{\frac{3}{2}}}} \right\rbrack}_{\frac{hl}{R}}^{\frac{hu}{R}}$

[0074] The integrand is evaluated between the normalized product heightsin inches, hu/R and hl/R, at the lower and higher ends of the tiltedtank, respectively. It is standard industry practice to install tanks onan incline to divert water and sludge away from the submersible pumps.

[0075] Tank tilt is identified from the pattern it induces in the recordof cumulative variances as a function of product height. It iscompensated for by fitting the correct mathematical form for height andvolume conversions in a titled tank to the cumulative variancecalculated by the method of least squares. This is done simultaneouslywith estimation of the initial inventory.

[0076] Tank length L and radius R are established by equating the firstpartial derivatives of the sum of squared cumulative variance withrespect to length and radius and determining the values which minimizethe sum of squared variances. Simultaneous estimation of initialinventory is also required when estimating tank length L and radius R.

[0077] Errors in measurement of the product height h in the tank arecharacterized by curvilinear patterns induced by height to volumeconversions in the cumulative variance for a cylindrical container whenheights are transposed upward or downward. Such errors also arecompensated for by minimizing the sum of squared cumulative varianceswith respect to increments or decrements to measured product height.This estimation also requires simultaneous estimation of the initialinventory of the tank.

[0078] In general, the accuracy of the estimates of the tank dimensions,tank orientation and height measurements is confirmed by observing thatthe cumulative variances of each derived value as a function of nominalproduct height are random and display no systematic influence oreffects.

[0079] Dispenser totalizer calibration is continuously monitored andevaluated by minimizing the sum of squared cumulative variances withrespect to multiplicative constants associated with individual reportedcumulative sales volumes from all pump dispensers associated with aparticular tank system. This eliminates the need for manual verificationof meter calibration.

[0080] In particular, gauge performance is continuously monitored toidentify gauge malfunctions or degradation in gauge performance.Monitoring of gauge performance is independent of diagnostics which areinternal to the measuring device. Diagnoses of problems are based onlyon their impact on the cumulative inventory variances which arecontinuously monitored by the software.

[0081] If the gauge fails to record changes in product height when thedispensers register sales, an increase in cumulative variancesapproximately equal to sales volume is observed; this effect can beidentified by the monitoring software and a warning of gauge malfunctiongenerated to the operator.

[0082] However, observation of the gauge registering product heightchange, but with a time lag after sales are recorded, may be a featureof normal gauge performance. Such normal gauge performance is identifiedby repeated positive increments in cumulative variances as sales arecompleted with subsequent return of the cumulative variance to normalbounds. When such gauge function is determined to be the normaloperating characteristic of a particular system, constrainedoptimization with lagged variables is introduced into the software.Otherwise, the gauge's performance is reported as a malfunction.

[0083] Finally, temperatures in the tank are monitored to detect changesthat are excessive for the time intervals between observations. Erratictemperature readings are deleted, and may indicate gauge malfunction.

[0084] The software computes actual, rather than nominal, deliveredquantities and requires no input by the system operator. The operatormay choose to input into the system the nominal delivery quantityindicated by the delivery invoice, along with the temperature andcoefficient of expansion of the product at the point of pick-up. Thesoftware will then compute overages or shortages between the nominal andactual quantities delivered, as well as the overages or shortages causedby temperature-induced variations in the transport of the product to thefacility and in the subsequent mixing of the delivered product with thatresident in the tank.

[0085] Delivery is identified by the software when a positive cumulativevariance is observed which exceeds the system noise level and is notsucceeded by a return to normal variance bounds. Delivered quantitiesare computed by estimating the volume increases they induce in multiple,successive observations. The required number of successive observationsis determined as that sufficient to generate a confidence width which iswithin a predetermined tolerance. The system of the present invention iscapable of accounting for sales conducted during delivery and for noiseintroduced by post delivery turbulence in the tank.

[0086] One-time unaccounted for removals or additions to the tank arecomputed in the same manner. Deliveries are distinguished from suchevents by computing the rate of input, which in the case of normalgravity delivery should exceed 100 gallons per minute. Other modes ofdelivery, e.g., pipeline delivery into above ground tanks, areidentified by incorporating their known delivery rates.

[0087] Leakage from the system is identified by a continuous linearnegative trend in the data which exceeds the computed minimum detectableleak rate after all of the various error phenomena described above havebeen identified and compensated for. This calculation deals with thetotality of the data obtained by constantly monitoring known removalsand is not restricted to observations made only when the system isdormant. It is also independent of any single data reconciliationcalculation in that trends throughout all of the data are evaluated.

[0088] All calculations concerning volumes are made on the basis of netvolume, according to the following definitions:

Net Volume in Tank=Gauge Volume(1−(1−( t−60)CE)

[0089] where:

[0090] t=Measured temperature in degrees Fahrenheit (if centigrade, theterm in parentheses becomes (t-15)).

[0091] CE=Coefficient of expansion

[0092] and

Net Sales Volume=Metered Sale(1−(0.5(t ₁ +t ₂)−60)CE)

[0093] where t₁ and t₂ temperatures measured by the tank gauge at thebeginning and ending of a sale transaction, respectively. Deliveries arecomputed in net gallons, but are converted to gross quantities ifrequired, based on external information input by the system operator, asfollows:

[0094] GT=Gross gallons on invoice at the originating terminal.

[0095] NT=Net gallons on invoice at the terminal.

[0096] tT=Temperature at the terminal.

[0097] CE=Coefficient of expansion.

[0098] The program also records:

[0099] tA=Ambient temperature in the tank prior to delivery.

[0100] tF=Temperature in the tank at the conclusion of delivery.

[0101] The following value is computed: $\begin{matrix}{{tS} = \text{Temperature~~of~~the~~product~~in~~the~~delivery~~vehicle~~at}} \\{\text{the~~facility~~at~~the~~beginning~~of~~delivery.}} \\{= {{tF} + {\frac{NVA}{NVD}\left( {{tF} - {tA}} \right)}}}\end{matrix}$

[0102] where: $\begin{matrix}{{NVD} = \text{Actual~~net~~volume~~delivered,~~previously~~computed.}} \\{{NVA} = \text{Net~~volume~~in~~the~~storage~~tank~~at~~the~~start~~of~~delivery.}} \\{\quad {{NS} = \text{Net~~overage (+)~~(underage (-))~~in~~delivery.}}} \\{= {{NT} - {NVD}}} \\{{GVD} = \text{Gross~~volume~~delivered.}} \\{= {{NVD}\left( {1 + {\left( {{tF} - 60} \right){CE}}} \right)}} \\{{GVS} = \text{Gross~~volume~~in~~the~~transport~~vehicle~~at~~the}} \\{\text{facility~~prior~~to~~delivery.}} \\{= {{NVD}\left( {1 + {\left( {{tS} - 60} \right){CE}}} \right)}} \\{{GSM} = \text{Shrinkage~~due~~to~~mixing~~in~~the~~tank.}} \\{= {{GVS} - {GVD}}} \\{{GVT} = \text{Actual~~gross~~volume~~in~~the~~transport~~vehicle}} \\{\text{at~~the~~facility.}} \\{= {{NVD}\left( {1 + {\left( {{tT} - 60} \right){CE}}} \right)}} \\{{GST} = \text{Shrinkage~~during~~transit~~to~~the~~facility.}} \\{= {{GVT} - {GVS}}} \\{{GOS} = \text{Gross~~overage~~(+)~~(underage~~(-))~~adjusted~~for}} \\{\text{temperature~~effects.}} \\{= {{GT} - {GVD} + {GST} + {GSM}}}\end{matrix}$

[0103] Calculations of volumes actually delivered are based on multipleobservations of the balance of measured tank volumes and cumulativesales. This method requires frequent simultaneous observations of salesand in-tank volumes (i.e. product heights) and temperatures.

[0104] The volume of product in a tank is derived by measuring theheight of the product and using the geometry of the tank, which isassumed to be known, to compute the corresponding volume. In manyinstances, tank dimensions vary substantially from assumed designdimensions. Regulatory specifications permit up to 10% variation inlength and diameter of cylindrical tanks.

[0105] Tank orientation can also cause complications in thecalculations. The volume corresponding to a measured height variessubstantially when the tank is tilted away from horizontal or rolledaway from vertical.

[0106] Further, tanks may also fail to conform to a known geometryeither through faulty manufacture or installation, or may suffersignificant deformation during the course of operations. For example,many fiberglass tanks sag or bend along their length.

[0107] In addition, installed tanks are typically inaccessible, anddifficult to measure. Thus, it is necessary to confirm the accuracy ofheight to volume conversions from generated inventory data and toidentify and correct discrepancies where they exist.

[0108] The foregoing problems are compounded when two or more tanks aremanifolded together. Manifolded tanks are joined together by pipingsystems and serve common dispensers. Thus, sales quantities formanifolded tanks constitute withdrawals from all tanks in the manifoldedsystem, but not necessarily in equal quantities. Product heightstypically vary from tank to tank, but tank geometries, dimensions andorientation may also vary so that a procedure for correcting height tovolume conversion errors for a single tank will not apply.

[0109] The different factors which influence inventory data manifestthemselves in distinct ways which facilitate their identification andcorrection. These factors are most easily identified by examination oftheir effects on cumulative departures of actual measured inventory froma theoretical or book value when viewed across a variety of dimensions.In particular, one-time undocumented physical additions or removals ofproduct, e.g. over or under deliveries and pilferage, are evidenced byan addition or subtraction of a constant quantity from the cumulativevariance at the time of occurrence and all subsequent observations.Continuous loss of product accumulating over time, e.g. leakage, isevidenced by a loss trend over time. Continuous loss of product varyingproportionally with sales value, such as a line leak or metermiscalibration, may be determined by identifying a constant negativetrend that is cumulative only over periods where delivery lines arepressurized.

[0110] A pattern of gains or losses, or both, recurring cyclically asthe tank is successively filled and emptied with no long term gain orloss of product, is the pattern associated with height to volumeconversion error. The pattern is cyclical because the error source isidentical in each cycle as the tank is filled or emptied. It isdistinguishable from the other patterns in that it retraces the samepath without the translation which would occur if physical loss or gainof product were taking place.

[0111] This problem is most readily diagnosed by analyzing cumulativevariance as a function of product height. If the variances are randomwith no evidence of systematic effects, height to volume conversions maybe assumed to be correct. If not, the form of the induced patternindicates the nature of the conversion error. Thus, an error in tanklength induces a linear pattern, an error in tank tilt induces asinusoidal pattern, and a constant error in tank height measurementinduces an arc-like pattern. When other sources of loss or gain arepresent, the conversion error patterns remain, but are translated ineach succeeding filling/emptying cycle to reflect the physical loss ofproduct which has occurred during that cycle. Thus, confusion betweenconversion errors and other effects can be eliminated.

[0112] Sales readings and product height measurements must be madesimultaneously. Since the number of observations in any one sales cycleis typically too few to generate a conversion table of sufficient detailto be of practical use, subsequent sales cycles and their correspondingdeliveries must be incorporated. If, however, deliveries are unmeteredand are used to approximate the volume (as is the standard industrypractice), significant inconsistencies are introduced. If an overage orshortage occurs during delivery, then all subsequent sales volumescorrespond to tank cross sections which have been shifted upward ordownward from their predecessors. Averaging or statistical treatmentcannot overcome this deficiency since there is no means of knowingwithout metering whether, by how much, and in what direction the datahas been shifted.

[0113] The procedure of the present invention may include determining ifheight to volume conversion error is a problem. If the error is aproblem, then the system must determine the nature of the problem, e.g.tank dimensions, tank orientation, height measurement or unknown tankgeometry, and whether the conversion problem is compounded by othergains and losses. If leakage is suspected, an on-site leak detectioninvestigation is undertaken. If no leakage is indicated, and one or allof tank dimensions, tank orientation and height measurement areproblems, new conversion factors are calculated and confirmed using thediagnostic procedures described herein.

[0114] If unknown tank geometry or manifolded systems are encountered,the exact current percentage of metered sales actually dispensed fromeach dispenser is determined by physical measurement. A high orderpolynomial using a variable of measured product height is used toconvert height to volume. The parameters of the polynomial are derivedfrom the differences between measured product height corresponding tothe beginning and ending of sales events which do not overlapdeliveries.

[0115] For a single tank, actual dispensed quantities are regressedusing a polynomial based on the differences in measured product heightbefore and after individual sales, subject to the constraint that whenthe polynomial is evaluated at a height equal to tank diameter, theresult is the total tank volume. Observations which include deliveryevents are discarded.

ASale _(i) =a ₁(h _(i−1) −h _(i))+a ₂(h _(i−1) ² −h _(i) ²)+ . . . +a_(n)(h _(i−1) ^(n) −h _(i) ^(n))

Vol=a ₁ d+a ₂ d ² + . . . +a _(n) d ^(n)

[0116] A fifth order polynomial has proven adequate in most cases.Residual analysis may be used to determine adequacy of the polynomial inthe presence of severe tank distortions, and higher order polynomialsmay be introduced as necessary. The number of observations required isdetermined by estimating a confidence bound around the resultingpolynomial with a width adequate for the desired resolution. Thus,

[0117] ASale_(i)=Actual dispensed volume in period i.

[0118] h_(i)=Product height upon conclusion of ASale_(i).

[0119] h_(i−1)=Product height prior to commencement of Asale_(i) andafter completion of Asale_(i−1).

[0120] d=Diameter of tank.

[0121] Vol=Total volume of tank.

[0122] The converted volume for height h is then given by:

Vol(h)=a ₁ h+a ₂ h ² + . . . +a _(n) h _(n) ^(n)

[0123] The omission of a constant term in the regression implies that

Vol(h)=0 when h=0

[0124] This ensures that the polynomial derived from the heightdifferences is well defined.

[0125] For manifolded systems, actual sales are regressed simultaneouslyon individual polynomials based on the various height differences in theseveral tanks which correspond to a particular sales volume, subject tothe constraint that each polynomial evaluated at the corresponding tankdiameter yields the total volume of that tank.ASale_(i) = a₁₁(h_(i − 11) − h_(i1)) + a₂₁(h_(i − 11)² − h_(i1)²)+  …   + a_(n1)(h_(i − 11)^(n) − h_(i1)^(n)) + a₁₂(h_(i − 12) − h_(i2)) + a₂₂(h_(i − 12)² − h_(i2)²)+  … + a_(n2)(h_(i − 12)^(n) − h_(i2)^(n))+  … + a_(1m)(h_(i − 1m) − h_(im)) + a_(2m)(h_(i − 1m)² − h_(im)²)+  … + a_(n  m)(h_(i − 1m)^(n) − h_(im)^(n))

[0126] where:

[0127] Asale_(i)=Actual Sales volume in period i.

[0128] h_(i−1j)=Height of product in tank j after completion ofAsale_(i−1) and prior to commencing Asale_(i).

[0129] j=1,2, . . . m

[0130] h_(ij)=Height of product in tank j after completion of ASale_(i).

[0131] m=Number of tanks manifolded.

[0132] Volume conversion or the m measured heights, h₁, h₂, . . . h_(m)in the total system is:${{Vol}\left( {h_{1},h_{2},\ldots \quad,h_{m}} \right)} = {\sum\limits_{i = 1}^{m}{\sum\limits_{j = 1}^{n}{a_{ji}h_{i}^{j}}}}$

[0133] where:

[0134] h_(i)=Height of product measured in the ith tank in the manifold.

[0135] Delivery inaccuracies have no impact on this calculation sinceall observations made during deliveries are discarded. Height changesare related only to the corresponding volumes dispensed.

[0136] Prior determination of actual quantities dispensed, as opposed tometered quantities, ensures that the only remaining source of error israndom measurement error. Regression is designed to accommodate randomerror of this kind to facilitate inferences when errors are present.

[0137] An alternative method of estimating volume of product based onproduct height in single or manifolded tanks involves determining avolume function by integrating a differential of the volume function.The total differential of the volume function is estimated using one ofseveral procedures, e.g., least squares estimation. For example, for amanifolded system of storage tanks, if

[0138]Sa _(i) =V(h _(1,i) , h _(2,i) . . . h _(m,i))−V(h _(1,i+1) , h_(2,i+1) , . . . , h _(m,i+1))

[0139] where Sa_(i) is the measured volume change associated withmeasured changes in product height during a dispensing event from themanifolded tanks, then $\begin{matrix}{{Sa}_{i} = {{V\left( {h_{1,1},h_{2,1},{\ldots \quad h_{m,1}}} \right)} - {V\left( {h_{1,{i + 1}},h_{2,{i + 1}},\ldots \quad,h_{m,{i + 1}}} \right)}}} \\{\approx {{{V_{1}\left( {h_{1,i},h_{2,i},\ldots \quad,h_{m,1}} \right)}\left( {h_{1,j} - h_{1,{i + 1}}} \right)} +}} \\{{{{V_{2}\left( {h_{1,i},h_{2i},\ldots \quad,h_{m,i}} \right)}\left( {h_{2,1} - h_{2,{i + 1}}} \right)} + \ldots \quad +}} \\{{{V_{m}\left( {h_{1,i},h_{2,i},\ldots \quad,h_{m,1}} \right)}\left( {h_{m,1} - h_{m,{i + 1}}} \right)}}\end{matrix}$

[0140] where V_(j)(h_(1,i), . . . , h_(m,i)) is the partial derivativeof the volume function with respect to h_(j), the height of the fluid inthe j^(th) tank. The least squares technique provides maximum likelihoodestimates because measurement errors occurring in tank gauges 20, 22, 24have been established to be normally distributed.

[0141] A differential function for a volume function having anyfunctional form may be estimated in this manner. For example, a highorder polynomial may be used and constrained to have a preset volume ata maximum height, zero volume for zero height in all tanks and/or zerovalue of the first derivative at maximum height and at zero height.

[0142] For example, if

[0143] h_(ij)=product height in tank i at the completion of sale j andprior to the start of sale j+1,

[0144] Sa_(k)=volume dispensed in sale k,

[0145] and the volume function is an r^(th) order polynomial in the form${{Vol}\left( {h_{1},h_{2},{\ldots \quad h_{m}}} \right)} = {\sum\limits_{i = 1}^{m}{\sum\limits_{j = 2}^{r}{a_{1,j}h^{j}}}}$

[0146] then${Sa}_{k} \approx {\sum\limits_{i = 1}^{m}{\sum\limits_{j = 1}^{r - 1}{\left( {j + 1} \right)a_{ij}{h_{ik}^{j}\left( {h_{i,{k - 1}} - h_{i,k}} \right)}}}}$

[0147] where the linear term of the polynomial is omitted to provide azero derivative at h=0. Then, the following equation may be minimized$\sum\limits_{k}\left( {{Sa}_{k} - {\sum\limits_{i = 1}^{m}{\sum\limits_{j = 1}^{r - 1}{\left( {j + 1} \right)a_{ij}{h_{ik}^{j}\left( {h_{i,{k - 1}} - h_{i,k}} \right)}}}}} \right)^{2}$

[0148] subject to${\sum\limits_{j = 1}^{r - 1}{\left( {j + 1} \right)a_{ij}h\quad \max_{ij}^{i}}} = 0$

[0149] and${\sum\limits_{j = 2}^{r}{a_{ij}h\quad \max_{i}^{j}}} = {{Vol}\quad \max_{i}}$

[0150] for

i=1,2, . . . , m

[0151] where hmax_(i) is the maximum product height in tank i,Volmax_(i) is the preset maximum volume in tank i and m is the number oftanks in the manifolded system.

[0152] The foregoing equation works well for m=1. For m>1, a furtherconstraint is required to ensure upward concavity of the individualvolume functions near zero volume. This is accomplished by constrainingthe second partial derivatives of the individual volume functions to bepositive at zero volume. In the case of polynomial functions and tankswith equal radii, this reduces to the constraint a_(1,1)=a_(2,1)= . . .=a_(m,1).

[0153] Alternatively, the volume function may take the form${V(h)} = \frac{^{f{(h)}}}{1 + ^{f{(h)}}}$

[0154] where f(h) is a function of the height h. The derivative of V(h)is${V^{\prime}(h)} = \frac{^{f{(h)}}{f^{\prime}(h)}}{\left( {1 + ^{f{(h)}}} \right)^{2}}$

[0155] Numerical minimization may be used to estimate this derivativefunction. An advantage of a function in the form of V′(h) is that itasymptotically approaches zero (0) near h=0 and one (1) near the maximumheight.

[0156] Determining the volume function by integrating an estimatedderivative of the volume function has many advantages. For example, thedata used to estimate the derivative consists of discrete measurementsof dispensed volumes and corresponding product height changes, whichavoids introducing ambiguities and errors due to inaccurate calculationsof deliveries of the product. The data does not need to be sequential,and data for periods during deliveries and post delivery turbulence maybe discarded. Because the only error sources are in the metering devices(for which calibration may be determined as described herein) and randomerrors of height measurement (the magnitude of which may be determinedas described herein) the error resulting from the height to volumeconversion may be contained within acceptable limits. Further, thevolume function derivative may be estimated accurately because thesystem can collect a large number of data points, which may be stored ina compressed format as described below, and because the system avoidsdelivery calculation errors. As in the case of calculating the volume ofproduct in a single tank, the sales, volume and tank height measurementsmust take place simultaneously, the calibration of individual metersmust be monitored and recorded, and a large volume of data must becollected and recorded.

[0157] With respect to temperature, the temperature of product deliveredinto a tank system almost invariably differs from the temperature of theproduct already in the tank. Its addition has the effect of expanding orcontracting the volume of the combined product. This change in volumecan create the appearance of incorrect dimensions of the height tovolume conversion, appear as leakage where none exists, or it can maskthe existence of actual leakage.

[0158] It is therefore preferable, and frequently essential, that allvolumes, sales, deliveries and product in storage be converted to acommon temperature prior to analysis. Typically 60° F. (15° C.)) ischosen as the standard. The conversion is accomplished as follows:

Net Volume=Gross volume(1−(t−60)CE)

[0159] where:

[0160] t=Measured product temperature in degrees Fahrenheit

[0161] CE=Coefficient of expansion.

[0162] As above, all calculations are in net gallons of product.

[0163] A complication to the calculation may occur if the tank gauges20, 22, 24 used to measure product volume are designed for static ordormant mode tank testing. Such tank gauges detect leakage when the tankis taken out of service. In this case, product volume changes due totemperature changes during the course of a test must be accounted for.

[0164] Further, as shown in FIG. 1, temperature sensors 46, 48, 50 arelocated at different heights in tank 12. If the level of product fallsbelow a given temperature sensor, the corresponding weighted temperaturemeasurement is dropped from the average temperature calculation, and atemperature jump and corresponding volume change may be observed whenthe net volume is calculated using the new weighted average oftemperatures. If uncorrected, such repeated jumps in the data wouldpreclude further analysis of the data for leak detection or thegeneration of height to volume conversions.

[0165] The system of the present invention may be used to overcome thesetemperature related problems. Using the following definition,

[0166] NDBN=The net cumulative variance in the inventory data atobservation N. then,${NDB}_{N} = {a\left( {1 - {\left( {t_{0} - 60} \right){CE}} - {\sum\limits_{i = 1}^{N}{{Sa}_{i}\left( {1 - {\left( {t_{i} - 60} \right){CE}}} \right)}} - {V_{N}\left( {1 - {\left( {t_{N} - 60} \right){CE}}} \right)}} \right.}$

[0167] where

[0168] a=Gross initial inventory.

[0169] t₀=Temperature of initial product volume.

[0170] t_(i)=Temperature of product at observation i.

[0171] Sa_(i)=Gross volume sold in period i.

[0172] V_(N)=Measured gross volume in tank at period N.

[0173] CE=Coefficient of expansion.

[0174] Absent random error or leakage, and assuming no deliveries ofproduct then

NDB_(N)=0

[0175] and${{a\left( {1 - {\left( {t_{0} - 60} \right){CE}}} \right)} - {\sum\limits_{i = 1}^{N}{{Sa}_{i}\left( {1 - {\left( {t_{i} - 60} \right){CE}}} \right)}}} \approx {V_{N}\left( {1 - {\left( {t_{N} - 60} \right){CE}}} \right)}$

[0176] Therefore, if a temperature jump to temperature t* occurs at anobservation N+1, then $\begin{matrix}{{NDB}_{N + 1} = {{V_{N}\left( {1 - {\left( {t_{N} - 60} \right){CE}}} \right)} - {{Sa}_{N + 1}\left( {1 - {\left( {t^{*} - 60} \right){CE}}} \right)} -}} \\{{V_{N + 1}\left( {1 - {\left( {t^{*} - 60} \right){CE}}} \right)}} \\{= {{V_{N}\left( {1 - {\left( {t_{N} - 60} \right){CE}}} \right)} - {{Sa}_{N + 1}\left( {1 - {\left( {t^{*} - 60} \right){CE}}} \right)} -}} \\{{\left( {V_{N} - {Sa}_{N + 1}} \right)\left( {1 - {\left( {t^{*} - 60} \right){CE}}} \right)}} \\{= {{V_{N}\left( {t^{*} - t_{N}} \right)}{CE}}}\end{matrix}$

[0177] When this final quantity NDB_(N+1) is added to the volume wherethe transition occurs between temperature sensors, and all subsequentvolumes, the effect of the transition is eliminated, and analysisproceeds as it would where individual temperature readings areavailable.

[0178] A large number of variables must be estimated by the software toimplement the SIR system of the present invention. For example, as manyas forty hoses and independent totalizers per tank system, as well asdeliveries numbering four or more per day must be accommodated. Thus, avery large volume of data must be accumulated, encompassing asubstantial spread of sales volumes from each totalizer for both theset-up analysis and subsequent routine monitoring. To accommodate thisvolume of data within current or conceivable future practical computermemory capabilities, the algorithm implemented by the software utilizesa matrix formulation which invokes the property of a sufficientstatistic to reduce the memory requirement.

[0179] The calculations used to determine the various error, loss trendand delivery estimate have the form: $\begin{matrix}{B = {\left( {x^{T}x} \right)^{- 1}x^{T}y}} \\{{MSE} = \frac{\left( {y - {xB}} \right)^{T}\left( {y - {xB}} \right)}{m + 1}} \\{S^{2} = {\left( {x^{T}x} \right)^{- 1}{MSE}}}\end{matrix}$

[0180] where:

[0181] B=column vector of m parameters to be estimated.

[0182] x=Matrix of parameter coefficients.

[0183] y=Column vector of independent variables.

[0184] MSE=Mean squared error.

[0185] S²=Variance covariance matrix of parameter estimates.

[0186] The values contained in vector y comprise tank gauge readings.The entries, in matrix x are measured sales volumes, time, and otherconstant values. The parameters of vector B which are to be evaluatedinclude the initial volume of the system and subsequent volume changes,including delivery amounts.

[0187] For example, if observations are recorded every minute, as manyas 1440 rows in the x matrix and the y vector may be recorded. It wouldclearly be impractical to accumulate and store data in that form over anextended period of time. Instead, data compression techniques areapplied so that only a manageable amount of data need be stored.

[0188] The algorithm utilizes the property that if an n×m matrix A ispartitioned into two submatrices, B and C, where B is an i×m matrix andC is a j×m matrix, such that i+j=n, then

C ^(T) C=A ^(T) A+B ^(T) B

[0189] For example, $\begin{matrix}{{{if}\quad C} = {{\begin{bmatrix}1 & 2 \\1 & 2 \\2 & 1 \\1 & 2\end{bmatrix}\quad {then}\quad A} = {{\begin{bmatrix}1 & 2 \\1 & 2\end{bmatrix}\quad {and}\quad B} = \begin{bmatrix}2 & 1 \\1 & 2\end{bmatrix}}}} \\{{A^{T}A} = {{\begin{bmatrix}1 & 1 \\2 & 2\end{bmatrix}\begin{bmatrix}1 & 2 \\1 & 2\end{bmatrix}} = \begin{bmatrix}2 & 4 \\4 & 8\end{bmatrix}}} \\{{B^{T}B} = {{\begin{bmatrix}2 & 1 \\1 & 2\end{bmatrix}\begin{bmatrix}2 & 1 \\1 & 2\end{bmatrix}} = \begin{bmatrix}5 & 4 \\4 & 5\end{bmatrix}}} \\{{Thus},{{{A^{T}A} + {B^{T}B}} = \begin{bmatrix}7 & 8 \\8 & 13\end{bmatrix}}} \\{{C^{T}C} = {{\begin{bmatrix}1 & 1 & 2 & 1 \\2 & 2 & 1 & 2\end{bmatrix}\begin{bmatrix}1 & 2 \\1 & 2 \\2 & 1 \\1 & 2\end{bmatrix}} = \begin{bmatrix}7 & 8 \\8 & 13\end{bmatrix}}}\end{matrix}$

[0190] At the conclusion of each 24 hour or other period, only x^(T)xand x^(T)y are computed and stored. The matrix x has the form of asquare n×n matrix. Further, the aggregates of observations for thedifferent periods are additive, since two square matrices having n×ndimensions may be added. Thus the total data storage requirement foreach period is determined only by the square of the number of parametersof interest.

[0191] The system is able to accommodate virtually unlimited numbers ofobservations by this method of data compression. Without thiscapability, the system would not have the storage capacity to accuratelyand simultaneously estimate the numbers of parameters which are requiredto perform a statistically significant calculation. This datacompression method also allows for processing the data at the facilityor for transmitting the data to a host computer for periodic analysis.FIGS. 2, 3 and 4 show the Mathcad computer code used to perform the datacompression algorithm.

[0192] Furthermore, (x^(T)x)⁻¹x^(T)y is a complete and sufficientstatistic for B. No statistically useful information is lost in thecompression. The overall procedure is, therefore, unlimited by memory.The only limitation remaining is the precision available in the computersystem used.

[0193] The software performs SIR analysis, including inventoryestimation and leak detection, using the above equation in the followingform: $\begin{matrix}{x = \begin{bmatrix}1_{range} \\{T_{range}\left( {- 1} \right)} \\{\left( {- 1} \right)\left( {- S_{range}} \right)^{\langle{meters}\rangle}}\end{bmatrix}} \\{y = {\left( {Stk}_{range} \right) - \left( {CD}_{range} \right)}}\end{matrix}$

[0194] where:

[0195] range=1 . . . (number of observations)

[0196] meters=1 . . . (number of dispensers)

[0197] and

[0198] I_(range)=Column of 1's.

[0199] T_(range)=Cumulative time in minutes.

[0200] (S_(range))^(<meters>)=Cumulative sales for an individualdispenser in gallons.

[0201] CD_(range)=Cumulative deliveries.

[0202] Stk_(range)=Tank stick reading in gallons.

[0203] To estimate the initial inventory, the matrix x includes a columnof unitary values. To estimate loss trends, the matrix x includes acolumn containing cumulative times of measurement and cumulative sales.The values of B, MSE and S² are then calculated, producing the followingresult for the vector B:

[0204] B₁=Estimated initial inventory.

[0205] B₂=Loss trend.

[0206] B_(2+meters)=Individual meter error.

[0207] B is the vector containing the parameter estimates, namelybeginning inventory, meter calibrations and loss rate. The loss rateestimate is in the second row (n=2). S² is the variance covariancematrix of the parameter estimates. Thus, S₂₂=(S² ₂₂)^(1/2) is thestandard deviation of the loss rate estimate. Finally, the minimaldetectable leak is defined as t_(α)S₂₂, where t_(α) is the (1−α)percentile of the Student's t distribution.

[0208] The software performs delivery calculations using the equation inthe following form: $\begin{matrix}{x = \begin{bmatrix}1_{range} \\{T_{range}\left( {- 1} \right)} \\{S_{range}\left( {- 1} \right)} \\D_{range}\end{bmatrix}} \\{y = \left( {Stk}_{range} \right)}\end{matrix}$

[0209] where:

[0210] range=1 . . . (number of records)

[0211] and

[0212] 1_(range)=Column of 1's

[0213] T_(range)=Cumulative time in minutes.

[0214] S_(range)=Cumulative sales in gallons.

[0215] D_(range)=0 where T_(range) is less than or equal to deliverytime and

[0216] 1 where T_(range) is greater than or equal to delivery time.

[0217] Stk_(range)=Tank stick reading in gallons.

[0218] The values of B, MSE and S² are then calculated, producing thefollowing result for the vector B:

[0219] B₁=Estimated initial inventory.

[0220] B₂=Loss trend.

[0221] B₃=Meter error.

[0222] B₄=Estimated delivery amount.

[0223] S² is the variance covariance matrix of the estimates. Thus,S₄₄=(S² ₄₄)^(1/2) is the standard deviation of B₄, the delivery volumeestimate. The delivery tolerance is B₄±t_(αS) ₄₄, where t_(α) is the(1−α) percentile of the Student's t distribution. Delivery tolerancescan be reduced to any desired value by increasing the number ofobservations used in the calculation.

[0224] The SIR analysis used by the method of the present inventioninvolves computing and comparing cumulative variances. When the initialset-up is complete, computed trend and meter calibrations are used toproject forward an expected cumulative variance, that is, the expectedvalue of the difference between gauge readings and computed inventory.Actual cumulative variances are then computed from all subsequent gaugeand meter readings and compared to the expected variance.

[0225]FIGS. 5, 6 and 7 show the routine operation procedure 100 followedby the software to perform this analysis. Data from the set-up of thesystem and the most recent analysis is entered into the program at step102. The data entered includes the tank type, tank dimensions, tanktilt, meter calibrations, mean square error and calculated trends. Atstep 104, three variables established as counters, Counter1, Counter2,and Counter3, are set at zero. The measurement data from the systemitself is entered at step 106, namely the readings from the dispensertotalizers, the product height and the product temperature.

[0226] The software computes the gross volume of the product, the mostrecent gross volume and the sales as measured by the individualdispensers at step 108. The software further manipulates the data atstep 110 by converting all gross volumes to net volumes, computingobservation to observation variance, and computing cumulative variance.The sign of the cumulative variance is recorded at step 112.

[0227] The program proceeds on the basis of the cumulative variance andthe value of Counter1 in steps 114, 120, 124, 128, 132, 136 and 140.Depending on the cumulative variance and the value of Counter1, theprogram analyzes the collected data at step 118 if it is a finalobservation (step 116), deletes the collected data (steps 122 and 134),performs the analysis for a delivery (step 126) (see below), or readsnew data (steps 116, 122, 130, 134, 138 and 142) upon updating the valueof Counter1 and other computational variables (i.e. index, sign indexand sign). In some cases, collected data is deleted (steps 122 and 134).

[0228] Upon computing the loss rate at step 144, the program reads newdata at step 146 if the loss rate is not greater than or equal to, e.g.,0.2 gallon per hour, otherwise it computes the trend of the data at step148. If at step 150 it is determined that the trend is greater than 0.2gallon per hour, a warning is issued at step 156. In either case, thesoftware continues to read and analyze the data at steps 152, 154, 158and 160 until the last observation.

[0229] The operation of deleting data 170 is shown in detail in FIG. 8.After performing similar analyses at steps 172, 174, 176, 180 and 184,using the indices and the values of the calculated standard deviationsas in the routine operation procedure described above, the values of thecounters are updated and new data is read at steps 178, 182, 186 and188. Data is deleted in accordance with steps 178, 186 and 188.

[0230] Finally, FIG. 9 shows the delivery calculation 190 in detail.After determining that the cumulative variance is greater than apredetermined value (three standard deviations) at step 192, the programdetermines whether the variance is greater than, e.g., 100 gallons perminute (step 194). If so, the delivery is recorded and the amountdelivered is determined at steps 202 through 220.

[0231] If there is a delivery in progress (step 202), data is read untila negative observation to observation variance is observed (step 204).The variance is monitored until the turbulence in the tank subsides(step 206). Thirty observations are read (step 208), and allobservations from 15 minutes before the delivery until the end of theturbulence observations are deleted (step 210). An indicator variable isintroduced with the turbulence observation, from which regressioncommences (step 212). The confidence bound on the indicator is computed(step 214). If the confidence bound is within a predetermined tolerance,the volume of the delivery is reported within the confidence bounds(step 220); otherwise, additional observations are added, and theconfidence bound is recomputed (step 218).

[0232] If the variance between data measurements is less than 100gallons per minute, the software determines whether the gauges areinoperative and reports them as being inoperative (step 198), orproceeds as in the routine operation procedure according to step 200 (inwhich there is a negative variance) depending on whether the observedvariance exceeds a predetermined value (within one standard deviation)at step 196.

[0233] In general, if observed variances are within three standarddeviations or other predetermined tolerance of the expected value, thedata is stored for future analysis. When cumulative variance exceedsthree standard deviations or other predetermined tolerance, differentsoftware programs are executed depending on the nature and magnitude ofthe department.

[0234] If within ten (or other predetermined) successive observationsafter the initial departure, the cumulative variance returns to withinthe tolerance range, all data from and including the initial departureand prior to the initial observation are deleted. The time extent andnumber of observations involved is recorded and stored for, e.g., adaily gauge performance report.

[0235] If all ten (or other predetermined) successive observationsremain outside the tolerance bound and the cumulative variances are ofthe same sign, a new trend line is initiated at the point of initialdeparture. After ten (or other predetermined) additional observations, athird trend line is initiated. If the increment to the overall trendestimated from the most recent observations is not significant, the mostrecent data is consolidated with the previous data and the process isrepeated until such time, if ever, that the current trend increment issignificant.

[0236] If the departure is positive, the system checks whether theproduct is being dispensed and whether the gauge height fails todecrease, reflecting removal from tank. If so, the tank gauge isreported to be inoperative.

[0237] If the gauge height is increasing, monitoring is continued asabove until the most recent trend line returns to its original slope.Minute to minute variances are monitored to detect turbulence until thegauge values again return to within tolerance. All observations whichoccurred in the fifteen minutes prior to the first positive departureuntil the end of post delivery turbulence are deleted. An indicatorvariable is introduced at the first observation after post deliveryturbulence. The system collects thirty additional observations andperforms the regression from the beginning of the period to determinethe volume delivered. The volume delivered is then reported.

[0238] If the departure is negative, the system proceeds as withdelivery. If successive slope increments fail to show a return to theoriginal slope, indicating continuing loss of product for apredetermined period, typically one hour, and the slope exceeds 0.2gallon per hour, the system reports a warning that there is a continuousloss of product. If the loss rate is less than 0.2 gallon per hour butgreater than the minimum detectable leak, the system continues tomonitor and recalculate the parameters, to be included in a dailyoperational report. If the incremental trend line shows a return to theoriginal trend, the system proceeds as with delivery, introduces anindicator variable, deletes data as necessary, and performs theregression to determine the volume of product removed. The systemreports a one-time removal of product.

[0239] Referring to FIG. 10, the invention incorporates a dataacquisition and transmission network (DAT network) 300 to completelyautomate the process of obtaining, capturing, transferring andprocessing product inventory data for use in product management,delivery scheduling and environmental compliance practices. DAT network300 includes on-site processors 302, 304 at the facilities 306, 308where the tanks are located, a customer host processor 310 and a centralhost processor 312. DAT network 300 links multiple remote facilities306, 308 to central host processor 312, which performs the SIR analysis.The link may be accomplished indirectly through customer host processor310, which itself is connected to a plurality of remote facilities 306,308. Each of these processor elements is composed of independentlyoperating software and hardware systems which form the basis of a widearea network linked by modems which transmit information electronicallyvia the telephone network 314 using standard dial-up voice gradetelephone lines. Examples of DAT networks are the TeleSIRA and ECCOSIRAsystems developed by Warren Rogers Associates, Inc., Middletown, R.I.According to another embodiment, DAT network 300 may capture datawirelessly through, for example, PCS, cellular, Bluetooth, 802.11, WiFi,Infrared, radio frequency, or other wireless technology.

[0240] DAT network 300 provides a uniform method of integratedmanagement for the widest possible variation of underground andabove-ground fuel storage, movement, and measurement systems. On-siteprocessors 302, 304 are capable of obtaining information from anyelectronic or mechanical control system, enabling DAT network 300 toaccommodate facility configurations that are unique to each facilitywhile presenting the information captured at remote facilities 306, 308to customer host processor 310 or central host processor 312 in auniform format.

[0241] On-line processors 302, 304 obtain and capture product inventorydata through the use of proprietary interfaces with external systems inuse at remote facility 306, 308, such as tank gauges and sales recordingdevices. On-line processors 302, 304 transfer captured informationdaily, weekly or monthly through the public switched telephone network314 to customer host processor 310 or central host processor 312 for usein inventory management, delivery scheduling and/or environmentalcompliance. On-site processors 302, 304 may be, e.g., touch-tonetelephones acting as sending units and Windows-based multi-line, voiceprompt/response PC's as the receiving units. On-site processors 302, 304may be designed to meet the specific needs of facilities 306, 308without requiring remote hardware at the facility in addition to thatalready present.

[0242] In particular, each of on-site processors 302, 304 typically isequipped with an alphanumeric keypad, a character display, a powersupply, four programmable serial communication ports, an internalauto-dial/auto-answer (AD/AA) modem and a local printer port (forconnection to a printer). The keypad and display allow for operatorconfiguration and manual entry of sales, delivery and tank level data.Use of an AD/AA 2400 baud modem allows multiple on-site processor 302,304 to share an existing voice grade telephone line by establishingcommunication windows to minimize attempted simultaneous use. Each ofthe programmable serial communication ports is independent, fullyprogrammable and governed by options selected at the facility oroff-site through modem access. Finally, on-site processors 302, 304 canprompt the facility operator to enter missing or suspect entries whenresults are outside the expected range. According to another embodiment,on-site processors 302, 304 may capture data wirelessly through, forexample, PCS, cellular, Bluetooth, 802.11, WiFi, Infrared, radiofrequency, or other wireless technology.

[0243] The use of customer host processor 310, which is capable ofreceiving, storing and processing information from multiple on-siteprocessors 302, 304, enables the management of a remote tank populationfrom a single point of contact. A database of information created bycustomer host processor 310 is the basis for all higher level productmanagement functions performed by DAT network 300. The database is alsothe basis for the environmental compliance analysis performed by centralhost processor 312.

[0244] The use of central host processor 312, which is capable ofreceiving, storing and processing the information in the databasecreated by customer host processor 310 for product management enablesDAT network 300 to achieve maximum results by utilizing the database forenvironmental compliance without additional remote facility informationor communication. Central host processor 312 is capable of transmittinga resulting database of the environmental analysis back to customer hostprocessor 310 for printing and other customer record-keepingrequirements.

[0245] The processor elements of DAT network 300 may exhibit otheruseful operational characteristics. To prevent unauthorized access toDAT network 300, a security access code for dial-up data transferfunctions is required. Under secured access, the baud rate, parity, stopbit parameters and communication protocol are determined at any ofon-site processors 302, 304, customer host processor 310 or central hostprocessor 312.

[0246] Another function of DAT network 300 is to monitor tank contentsgenerally. DAT network 300 can be programmed to activate, e.g., anaudible and visual alarm if the water level in the tank is too high(e.g., greater than 2 inches), if the product level in the tank is toohigh (e.g., more than 90% of tank capacity) or too low (e.g., less than10% of tank capacity, more product must be reordered, or less than twodays supply), and if a theft occurs (product level changes during quietperiods).

[0247] The system may be used to obtain valuable information other thaninventory regulation and leak detection. For example, the system mayincorporate time series analysis routines, including Box Jenkins, movingaverage and exponential smoothing, to derive estimates of demand for theproduct which also incorporate temporal and seasonal trends and specialevents.

[0248] The demand analysis may also be combined with additional inputsof holding costs, reorder costs, transportation costs and penalty costsfor running out of stock. The system can include optimal inventoryalgorithms to determine optimal order quantities, reorder points andoptimal delivery truck routing. Further, the system may incorporatemultiechelon, optimal inventory procedures to accommodate combinedwholesale and retail operations, such as with calculus-basedoptimization and linear, nonlinear and dynamic programming.

[0249] As shown in FIG. 13, a DAT network may include a fuel accesscontrol unit or system 510 at a storage tank facility 500 such as a USTautomobile fueling facility. Fuel access control unit 510 is adispensing system actuated by the use of a device coded withinformation, e.g., a card 520 with a coded magnetic stripe 522, e.g., anoptical punched card, an electrically erasable programmable read-onlymemory (EEPROM) key, a radio frequency identification (RFID) tag, amagnetic resonance coupler, a bar code, or other type of coded mediumwhich contains identification information pertaining to the user. Fuelaccess control unit 510 may include apparatus for a user to inputinformation, e.g., a card reader 512, a display 514, and a keypad 516, acontrol system 562 for turning a fueling dispenser 560 on and off, and aprocessor 564 or similar computing platform for controlling andmonitoring the user's fueling process. Manufacturers of fuel accesscontrol system which rely upon optical reading devices or magneticstripe card reading devices to identify the user include FillRite, FuelMaster, Gasboy, PetroVend and Trak Engineering.

[0250] Fuel access control unit 510 is used to monitor the activity offueling dispenser 560. Fueling dispenser 560 includes a hose 566 fordispensing fuel from a tank 515, a totalizer 568 and a meter 569 formeasuring the volume of fuel dispensed by hose 566. Fuel access controlunit 510 may communicate with an on-site processor 530 located insidefacility building 540 over a local area network (LAN). Thecommunications between fuel access control unit 510 and on-siteprocessor 530 may be over RS-232/RS-485/RS-485 (MultiDrop) cabling 542.According to other embodiments, communications between fuel accesscontrol unit 510 and on-site processor 530 may occur wirelessly using,for example, PCS, cellular, Bluetooth, 802.11, WiFi, Infrared, radiofrequency, or other wireless technology.

[0251] Fuel access control unit 510 provides a system of controllingaccess to fueling facility 500 by determining the identity of each userof the facility and screening each user based on his or her authority topurchase fuel. Identification of the user is made by requiring the userto present a valid magnetic card (e.g., card 520), an optical punchedcard, an EEPROM key, an RFID tag, a magnetic resonance coupler, a barcode, or other type of coded medium, which contains identificationinformation pertaining to the user. Such fuel access control systems arereferred to as island control units or cardlock system. Further, theuser may be required to present additional identifying data by othermeans such as buttons, key switches, or by entering information onkeypad 516. Once the identification data is collected, fuel accesscontrol unit 510 determines the user's fueling privileges, and based onthis information will either allow or deny fueling by the user. Iffueling is allowed, fuel access control unit 510 will enable dispensingpump 560 for that particular user and monitor the fueling process. Atthe completion of the fueling process, fuel access control unit 510 willrecord the amount of the fueling transaction in a memory 567 and retainthe recorded information for further accounting of the transaction.

[0252] Fuel access control until 510 may be used to perform a variety offunctions, including the following:

[0253] 1. Identifying the user by reading a card or other coded mediumand collecting the user's identification information such as a driverlicense number or other personal data.

[0254] 2. Collecting other pertinent data for analysis, such as anidentification of the user's vehicle, the vehicle's odometer reading, atrip number, the trailer hub counter, the engine hour reading and/or arefrigerator unit hour reading.

[0255] 3. Making authorization decisions, to determine whether theidentified user is permitted to obtain fuel.

[0256] 4. Enabling fueling by enabling the proper dispensing pump forthe user.

[0257] 5. Monitoring fueling by controlling the maximum amountdispensed.

[0258] 6. Turning off the dispensing system if no fuel is dispensed fora predetermined period of time.

[0259] 7. Recording the fueling transaction by storing the final amountof fuel dispensed.

[0260] 8. Reporting the fueling transaction to a processing location forinventory analysis or other analysis.

[0261] There are two types of authorization procedures for determiningwhether an identified user is permitted to obtain fuel from a fuelingfacility. Fuel access control unit 510 may use either or both of theseauthorization procedures. For the first method of authorization,external authorization, fuel access control unit 510 collects the user'sinformation and forwards the collected information to an outside agentto make a final decision as to whether or not the identified user ispermitted to obtain fuel from fueling facility 500. The outside agentmay return an approval, along with fueling parameters (i.e., a maximumamount), or a denial. Fuel access control unit 510 will then inform theuser whether or not fuel may be obtained. The outside agent may beconnected to fuel access control unit 510 via a dial-up telephone line,a LAN or a direct communication link.

[0262] For the second method of authorization, internal authorization,fuel access control until 510 collects the user's information andcompares the collected information to a data table stored locally tomake the final decision as to whether or not to allow fueling. Thelocally stored table may return an approval, along with fuelingparameters (i.e., a maximum amount), or a denial. Fuel access controlunit 510 will then inform the user whether or not fuel may be obtained.The locally stored table may be housed directly in fuel access controlunit 510, in a control device at the fueling facility such as on-siteprocessor 530 or carried on the access medium (e.g., card 520) used torequest fueling authorization. The locally stored table may also beimbedded directly in fuel access control unit 510 or accessed via a LANinside the fueling facility's building 540.

[0263] Fuel access control unit 510 functions as an additional point ofsale (POS) device, similar to sales recording device 71 (FIG. 1). Fuelaccess control unit 510 responds to requests for hose status andtotalizer and meter values in the same manner as a POS device. Fuelaccess control unit 510 also monitors each hose 566 and tracks statuschanges in the hose, including indications that the hose is idle, that arequest for access is in process, that the use of hose 566 has beenauthorized, that the hose has been taken off its hook, that dispensingpump 560 is dispensing fuel with hose 566 removed from its hook, andthat the dispensing pump has been turned off and the hose is idle again.

[0264] Each detailed transaction that is completed by fuel accesscontrol unit 510 may be retrieved by on-site processor 530 from memory567 upon completion of the transaction. The transaction information maybe stored in processor 530 for further analysis. Further, based on thestored, detailed transaction information, a detailed site dispensingaudit can be performed. Such a site dispensing audit would determinewhether the volume claimed to be dispensed by fuel access control unit510 actually represents the volume change in the UST or AST during thesame period as calculated by on-site processor 530.

[0265] In conventional cardlock applications as well as othertransaction authorization procedures, the processing methods assume thatthe volume as determined by fuel access control unit 510 is accurate,but have no way of determining if any errors in calculating the volumehave occurred. A fuel access control system interfaced directly with anon-site processor 530 that receives data from an automatic tank gauge580 may also experience similar errors associated with conventionalinventory control practices. By contrast, an enhanced, integrated fuelaccess control unit 510 may include an accurate analysis of the state ofhose 566. Such an integrated fuel access control unit 510 may include anaccurate analysis of the state of hose 566. Such an integrated fuelaccess control unit 510 may avoid the occurrence of dispensing pump 560being properly accessed and enabled by authorization control system 562,but appearing not to be dispensing fuel. From the point of view of fuelaccess control unit 510, the user may have simply changed his mind aboutpurchasing fuel. However, from the perspective of on-site processor 530,a determination can be made about the dispensing pump's activity byanalyzing the tank activity and comparing that information to theactivity of totalizer 568 and meter 569. Further, although other hosesmay be actively dispensing fuel during the same period, on-siteprocessor 530 may track all hose activity independently for analysis.

[0266] Other embodiments are within the scope of the claims.

What is claimed is:
 1. A method of monitoring a fluid storage anddispensing system, the system comprising measurement apparatus formeasuring a volume of fluid associated with the system and a pluralityof temperature sensing devices disposed at a plurality of locationswithin the system, the method comprising: collecting a plurality ofmeasurement data from the measurement apparatus and the plurality oftemperature sensing devices in a form readable by a computer; storingthe plurality of measurement data in a compressed matrix format in acomputer memory; and statistically analyzing the compressed matrixformat to determine operational monitoring information and to calculatethe volume of fluid based on the measurement data collected from themeasurement apparatus and the plurality of temperature sensing devices.2. The method of claim 1 wherein the statistically analyzing stepincludes determining a correction value for the calculated volume basedon a weighted average of the temperature of the fluid simultaneouslymeasured at the plurality of locations within the system.
 3. The methodof claim 1 further comprising determining the presence of operationaldefects in the system based on the operational monitoring information.4. The method of claim 1 further comprising monitoring the accuracy ofthe measurement apparatus and the plurality of temperature sensingdevices based on the operational monitoring information.
 5. The methodof claim 1 further comprising determining whether a quantity of fluidremoved from the system is caused by a leak in the system based on theoperational monitoring information.
 6. The method of claim 5 furthercomprising delivering a warning if a leak is determined to exist in thesystem.
 7. The method of claim 1 wherein the collecting step isperformed continuously at periodic intervals.
 8. The method of claim 1further comprising querying the measurement apparatus and the pluralityof temperature sensing devices under the control of the computer.
 9. Themethod of claim 1 wherein the storing step comprises generating thecompressed matrix format as a product of a data matrix and the transposeof the data matrix.
 10. The method of claim 9 wherein the product isformed by addition of partial products of each of a plurality ofpartitions of the data matrix with the transpose of each partition. 11.The method of claim 1 further comprising transmitting the measurementdata to a host processor to perform the statistically analyzing step.12. The method of claim 11 wherein transmitting the measurement dataincludes wireless transmission.
 13. The method of claim 1 furthercomprising transmitting the compressed matrix format to a host computerto perform the statistically analyzing step.
 14. A method of monitoringa fluid storage and dispensing system, the system comprising a pluralityof measurement apparatus for measuring a volume of fluid associated withthe system, the method comprising: simultaneously collecting measurementdata from the plurality of measurement apparatus in a form readable by acomputer to determine a change in the volume; repeating the collectingstep to obtain a plurality of the measurement data; storing theplurality of measurement data in a compressed matrix format in acomputer memory; and statistically analyzing the compressed matrixformat to determine operational monitoring information.
 15. The methodof claim 14 further comprising estimating an initial value of the volumeduring the statistically analyzing step based on the operationalmonitoring information.
 16. A method of monitoring a fluid storage anddispensing system, the system comprising measurement apparatus formeasuring a volume of fluid associated with the system and a pluralityof temperature sensing devices located at different heights in thesystem, the volume having a height in the system, the method comprising:collecting a plurality of volume measurement data from the measurementapparatus in a form readable by a computer; adjusting the volumemeasurement data based on temperature measurements taken from those ofthe plurality of temperature sensing devices at a height below theheight of the volume in the system; storing the plurality of volumemeasurement data in a compressed matrix format in a computer memory; andstatistically analyzing the compressed matrix format to determineoperational monitoring information.